Statistical analysis. We test \(H_0\)  using the Friedman aligned ranks test [1]} and find that we can easily reject the null hypothesis of no difference with \(p\) -value \(< 2 \times 10^{-16}\) .We conducted our statistical testing in R using the stats library where \(2 \times 10^{-16}\)  is considered the minimum \(p\) -value. Values smaller than this are indicated with the `\(<\) ' symbol in printed output.
Having rejected \(H_0\) , we perform additional pairwise tests using the \(p\) -value adjustment procedure for multiple testing from [2]}.
For XGBoost and RF significant differences are detected between the generation methods (all \(p\) -values \(< 2 \times 10^{-16}\) ), providing evidence that background extent limitation can be of benefit to these ensemble methods.
However, for LR, environmental profiling was instead found to significantly improve performance irrespective of whether background extent limitation was used or not (\(p\) -values provided in SM).
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